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...It can be used for traditional cases like medical survival time, but also for business and product questions such as churn, subscription length, equipment failure, and customer retention. The library includes estimators such as Kaplan-Meier, Nelson-Aalen, and regression-based survival models. It is designed to be accessible to Python users and works well with common scientific computing workflows. Built-in plotting methods and datasets help users explore survival curves and compare groups visually. It is a practical tool for analysts, researchers, and data scientists who need event-time modeling without leaving Python.

...The tool treats most data interactions as streams, allowing flexible routing, storage, and visualization of metrics generated during model training. A distinctive capability is its “lazy logging” mode, which lets users query live training processes without pre-instrumenting all metrics ahead of time. TensorWatch supports multiple chart types and can be extended with custom visualizers and dashboards, making it highly adaptable for research workflows. Overall, the project acts as a powerful observability layer for ML experimentation, helping practitioners diagnose model behavior and compare runs more efficiently.

Adele was developed to simplify the daily work with data. Use it as a swiss knife to fill the gap between your work with spreadsheet application like MS Excel and enterprise servers like SAP ERP. Specialized tools like Rapid Miner, KNIME or similiary stuff should not be replaced. But Adele is designed for business people working with spreadsheet applications to analyse their data. There are many technical concepts in an easier way included. For example realtime OLAP, transformations,...